Samuel D. Hannah, A. J. Deepa, Varghese S. Chooralil, S Sangeetha · 11 authors
Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.
With the popularity of IoT devices and cloud technology in the medical industry. Sharing EHRs (Electronic Health Records) among medical institutions improves the accuracy of medical diagnosis and promotes the development of public medical. However, it is difficult to share EHRs among hospitals, and patients typically don’t know about the usage of their health records. In this paper, we propose a patient-controlled EHRs sharing scheme based on cloud computing collaborating blockchain technology. The medical abstract and the access strategy are stored in the blockchain to avoid being tampered with. To achieve the fine-grained access control, we propose the attribute-based encryption scheme and multi-keyword encryption scheme to encrypt EHRs. Moreover, we proposed a node-state-checkable Practical Byzantine Fault Tolerance consensus algorithm (sc-PBFT) to prevent the Byzantine nodes from sneaking into the consortium blockchain. First, we check the state of the elected master node to avoid the master node having any malicious records. Then, using pre-prepared, prepare, and commit processes to complete the consensus request submitted by the client. At last, the proposed consensus algorithm evaluates the state of the master node according to the completion of the three-stage process to reduce the impact of the malicious node on the whole consortium blockchain. By doing this, the malicious node will be marked and isolated into the isolation area. The experimental results show that the proposed sc-PBFT algorithm has better handling capability and lower consensus latency. Compared with the PBFT algorithm in the case of Byzantine nodes, sc-PBFT not only improves the robustness of the consortium blockchain network but also improves the handling capability.
Abdullah Ayub Khan, Asif Ali Laghari, Zaffar Ahmed Shaikh, Zdzisława Dacko-Pikiewicz · 5 authors
With the rapid enhancement in the design and development of the Internet of Things creates a new research interest in the adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of industrial Internet of Things and the security-related resource constraints of industrial 5.0. This conducts new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is an emerging limitation between the inadequate performance of industrial IoT and connected nodes, and the high resource requirement of permissioned private blockchain ledger has not yet been tackled with the complete solution. Due to the introductions of NuCypher Re-Encryption infrastructure, hashing tree and allocation, and deployment of blockchain proof-of-work required more computational power as well. This paper is divided into three different folds; first, we studied various related literature of blockchain-enabling industrial Internet of Things and its critical implementation challenging aspects along with the solution. Secondly, we proposed a blockchain hyperledger sawtooth-enabled framework. This framework provides a secure and trusted execution environment, in which service delivery mechanisms and protocols are designed with an acknowledgment, including the immutable ledger storage security, along with the peer-to-peer network on-chain and off-chain communication of industrial activities. Thirdly, we design pseudo-chain codes and consensus protocols to provide smooth industrial node streamline transactions and broadcast content. The proposed multiple proof-of-work investigated and simulated using Hyperledger Sawtooth-enabled docker for testing to exchange information between connected devices of industrial Internet of Things within the limited usage of resource constraints.
Blockchain technology has the characteristics of decentralization, traceability and tamper proof, which creates a reliable decentralized transaction mode, further accelerating the development of the blockchain platforms. However, with the popularization of various financial applications, security problems caused by blockchain digital assets, such as money laundering, illegal fundraising and phishing fraud, are constantly on the rise. Therefore, financial security has become an important issue in the blockchain ecosystem, and identifying the types of accounts in blockchain (e.g. miners, phishing accounts, Ponzi contracts, etc.) is of great significance in risk assessment and market supervision. In this paper, we construct an account interaction graph using raw blockchain data in a graph perspective, and proposes a joint learning framework for account identity inference on blockchain with graph contrast. We first capture transaction feature and correlation feature from interaction graph, and then perform sampling and data augmentation to generate multiple views for account subgraphs, finally jointly train the subgraph contrast and account classification task. Extensive experiments on Ethereum datasets show that our method achieves significant advantages in account identity inference task in terms of classification performance, scalability and generalization.
Moayad Aloqaily, Ouns Bouachir, Ismaeel Al Ridhawi
Advanced services leveraged for future smart cities have played a significant role in the advancement of 5G networks towards the 6G vision. Interactive immersive applications are an example of those enabled services. Such applications allow for the interaction between multiple users in a 3D environment created by virtual presentations of real objects and participants using various technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Digital Twin (DT) and holography. These applications require advanced computing models which allow for the processing of massive gathered amounts of data. Motions, gestures and object modification should be captured, added to the virtual environment, and shared with all the participants. Relying only on the cloud to process this data can cause significant delays. Therefore, a hybrid cloud/edge architecturewith an intelligent resource orchestration mechanism, that is able to allocate the available capacities efficiently is necessary. In this paper, a blockchain and federated learning-enabled predicted edge-resource allocation (FLP-RA) algorithm is introduced to manage the allocation of computing resources in B5G networks. It allows for smart edge nodes to train their local data and share it with other nodes to create a global estimation of future network loads. As such, nodes are able to make accurate decisions to distribute the available resources to provide the lowest computing delay.
Zhonghua Zhang, Jie Feng, Qingqi Pei, Le Wang · 5 authors
Blockchain and multi-access edge computing (MEC) are two emerging promising technologies that have received extensive attention from academia and industry. As a brand-new information storage, dissemination and management mechanism, blockchain technology achieves the reliable transmission of data and value. While as a new computing paradigm, multi-access edge computing enables the high-frequency interaction and real-time transmission of data. The integration of communication and computing in blockchain-enabled multi-access edge computing networks has been studied without a systematical view. In the survey, we focus on the integration of communication and computing, explores the mutual empowerment and mutual promotion effects between the blockchain and MEC, and introduces the resource integration architecture of blockchain and multi-access edge computing. Then, the paper summarizes the applications of the resource integration architecture, resource management, data sharing, incentive mechanism, and consensus mechanism, and analyzes corresponding applications in real-world scenarios. Finally, future challenges and potentially promising research directions are discussed and present in detail.
G. Indra Navaroj, E. Golden Julie, Y. Harold Robinson
Blockchain is a distributed ledger or data structure. Combined with many other technologies, it uses the internet of things, cloud computing, artificial intelligence, big data, and machine learning. Several industries, especially governments, have employed blockchain technology to overcome a variety of security challenges. Blockchain focuses on double-spending and distributed consensus. However, blockchain networks are inefficient and scalable. Communication overhead occurs due to many replications. This paper proposes an adaptive practical Byzantine fault tolerance algorithm in permission blockchains. This method divides the node into trust nodes and faulty nodes. The nodes with faulty reputations are excluded from voting. Also, the identified trust node has a high reputation in the consensus process. A majority of voting values select the master node. This adaptive PBFT algorithm is excellent for long-term periodicity and increased scalability, and lower overall communication costs. Finally, the performance of adaptive PBFT is compared to other algorithms.
Blockchain networks are mainly used in financial verification processes, the most famous of which is the Bitcoin network consisting of a huge number of nodes. Expansion of blockchain technology, however, to other areas, such as healthcare, arts, culture, and entertainment, is being considered internationally. Especially important challenges for ensuring the reliability of blockchain networks and the security of peer-to-peer networks are shortening the block propagation times throughout a blockchain network to reduce fork and preventing malicious eclipse attacks against targeted nodes in a network. Previous methods have tried to increase the block propagation speed at the expense of imposing a higher burden on each node and a higher risk of eclipse attack. This paper proposes a new neighbor selection method based on the neighbor's regional information. That is, each node has a relatively small number neighbors located outside its region. By using this simple method, the distribution of blocks throughout the network becomes faster and the random neighbor selection nature in a blockchain network is kept intact; thus, risk of eclipse attack is low.
T. Saravanan, A. Ambikapathy, Ahmad Faraz, Himanshu Singh
In the modern world, humans were inventing and discovering many new inventions, but they never concern about their health. Health is the main thing for a human, by that they can achieve various goals in the competitive earth. Blockchain is a decentralized technology that has the digital records of the data that are stored as blocks using a cryptographic hash. Internet of Things (IoT) is a collection of Wi-Fi sensors, actuators, software, and computer devices. After that, it is incorporated with mobile devices for various purposes. Big data is also an emerging that tends to analyze, extract information from complex databases to achieve high output with data security and confidentiality. By the integration of both, IoT and big data provide many advanced features for the current century that will enhance to improve various applications like health care, automobile, financial, and risk management. In this chapter, we investigate peer-to-peer cryptographic secured blockchain technology for IoT and big data in healthcare applications like collecting the data, analyzing the data, and providing full-pledged care to the patients by the healthcare management systems by the integration of IoT and big data to provide high privacy, authentication, integrity, and nonrepudiation to the applications. And also, it summarizes about concept of healthcare applications, blockchain technology proposed architecture, working model, and future works.
In order to ensure security and ledger consistency, in the identity management system, asymmetric cryptography and distributed consensus algorithms have been proposed. These advancements will allow user to have digital ID on his own device, like a smartphone, which he can share with service providers conveniently and securely through a DLT.
With the development of blockchain technology, people always expect that blockchain can be applied to other fields. However, the low Transaction Processing Speed (TPS) and broadcast delay of blockchain still restrict the application of blockchain. To solve these problems, we propose a new scheme named GVScheme to improve the scalability of blockchain network. GVScheme introduces the role of guarantor based on trust value mechanism. The guarantor node will guarantee the block spread in the network. When the node receives the guarantee block from the guarantor node, the order of verification block and propagation block will be determined according to the trust value of the guarantor. By reducing the block verification time, the block propagation delay in the network will also be reduced. It is worth mentioning that our scheme keeps the minimum modification to the blockchain, and may even be directly applied to the blockchain network. Simulation results show that GVScheme can effectively reduce block propagation delay and the fork rate in blockchain network. When the block size and the number of nodes increase, GVScheme also shows great performance. Thus, under the same fork rate, the blockchain using GVScheme can allow less mining interval and larger block size limit.
The database design based on blockchain is regarded as a distributed ledger combined with technologies such as distributed storage and encryption algorithms. It has the characteristics of decentralization and high security. Multiple entities of the Consortium Blockchain are subject to centralized supervision and have strict access mechanisms. Therefore, this paper attempts to study the data management model based on the Consortium Blockchain in the context of education big data, with the school archive management as the entry point, which is a safe and reliable information system with low data sharing cost, non-tamperable, and traceable. Design and application exploration route.
Muhammad Shafay, Raja Wasim Ahmad, Khaled Salah, Ibrar Yaqoob · 6 authors
Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today’s deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.
Currently many enterprises face issues regarding insufficient data collection samples and data recording dimensions, thus it's hard to make efficient predictions. Since it is limited by the requirement of protecting privacy and trade secrets, data can't be effectively shared among enterprises. Federated learning is an effective method to solve this problem, but there are some performance bottlenecks, information security issues and data trust issues still existed, which need to be improved in combination with other advanced technologies to meet the practical requirements. This paper combines the blockchain technology with federated learning technology, and uses decentralized blockchain system to replace the traditional centralized federated learning architecture. We adopt training method of updating models to achieve machine learning. In this way, we can avoid transmission of intermediate computing data and achieve mechanism of node access, model evaluation, motivation and audit with combination of block chain. In terms of the algorithm, the horizontal federated learning adopts the integrated learning algorithm, and the vertical federated learning adopts the deep learning algorithm. It will be described in detail below.
Abstract In recent times, advanced developments in healthcare sector result in the generation of massive amounts of electronic health records (EHRs). EHR system enables the data owner to control his/her data and share it with designated people. The vast volume of data in the healthcare system makes it difficult for data to ensure security and diagnostic processes. To resolve these issues, this paper develops a new hyperledger blockchain enabled secure medical data management with deep learning (DL)-based diagnosis (HBESDM-DLD) model. The presented model involves distinct stages of operations such as encryption, optimal key generation, hyperledger blockchain-based secure data management, and diagnosis. The presented model allows the user to control access to data, permit the hospital authorities to read/write data, and alert emergency contacts. For encryption, SIMON block cipher technique is applied. At the same time, to improve the efficiency of the SIMON technique, a group teaching optimization algorithm (GTOA) is applied for the optimal key generation of the SIMON technique. Moreover, the sharing of medical data takes place using multi-channel hyperledger blockchain that utilizes a blockchain for storing patient visit data and for the medical institutions to record links for the EHRs saved in external databases. Once the data are decrypted at the receiving end, finally, variational autoencoder (VAE)-based diagnostic model is applied to detect the existence of the diseases. The performance validation of the HBESDM-DLD model takes place on benchmark medical dataset and the results are inspected under various performance measures. The experimental results proves that the HBESDM-DLD methodology is superior to state-of-the-art methods.
Technologies such as distributed ledger and its utilization of blockchain have higher network security, promote the balance between security and efficiency, and new technical measures are being taken to enhance its security. However, blockchain technology and utilization are not secure. The security architecture of blockchain should be constructed from the concept of “zero-trust security”, and the security utilization of the blockchain should be highly valued from the network security strategy to promote international cooperation in blockchain security.
Omar A. Alzubi, Jafar A. Alzubi, K. Shankar, Deepak Gupta
Abstract Advancements in information technology have benefited the healthcare industry by providing it with distinct methods of managing medical data which improve the quality of medical services. The Internet of Things (IoT) and artificial intelligence are the foundations for innovative sustainable computing technologies in e‐healthcare applications. In the IoT‐enabled sustainable healthcare system, the IoT devices normally record the patient data and transfer it to the cloud for further processing. Security is considered an important issue in the design of IoT networks in the healthcare environment. To resolve this issue, this article presents a novel blockchain and artificial intelligence‐enabled secure medical data transmission (BAISMDT) for IoT networks. The goal of the BAISMDT model is to achieve security and privacy in reliable data transmission of the IoT networks. The proposed model involves a signcryption technique for secure and reliable IoT data transmission. The blockchain‐enabled secure medical data transmission process takes place among the IoT gadgets and service providers. The blockchain technique is applied to generate a viable environment to securely and reliably transmit data among different data providers. Next to the decryption process, the modified discrete particle swarm optimization algorithm with wavelet kernel extreme learning machine model is applied to determine the presence of disease. An extensive set of simulations were carried out on a benchmark medical dataset. The experimental results analysis pointed out the superior performance of the proposed BAISMDT model with the accuracy of 97.54% and 98.13% on the applied Heart Statlog and WBC dataset, respectively.
These days, security cameras perform surveillance tasks in institutions of education. Thanks to the utilization of analytic functions, such as headcount, face detection, face recognition, and the Black and White List, a possibility is given for composing an attendance register of the students visiting the lectures.To attain secure data storage, the utilization of decentralized blockchain technology is expedient. The most optimal solution is that the university would constitute a blockchain, so the institution would regulate the rules of the operation itself.To link the blockchain with the NVR (Network Video Recorder) unit, and to have the attendance register composing system running, a smart contract is to be implemented in practice. By the closing of a smart contract, great care should be applied to the input of valid data.
Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Chun‐Wei Tsai, Yi‐Ping Phoebe Chen, Tzu‐Chieh Tang, Yuchen Luo
The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain.
R Nilaiswariya, J. Manikandan, P. Hemalatha, Leema Roselin G
Nowadays, dealing with medical data is very difficult task. Because medical dataset contains very sensible details. And also handling of this crucial data is another difficult task. While dealing and handling with such data’s, data leakage problem may occur which paves the way to unstable the cloud environment. Existing approaches enables blockchain technology over cloud environment by processing large amounts of raw data which in turn reduces the overall network performance. In order to overcome these issues, we suggest a blockchain technology combined with Recurrent Neural Networks. The main objectives of the proposed system are two folded. Initially, we classify the data into high priority and low priority using Recurrent Neural Network (RNN) to improve the performance and scalability. Second, the blockchain technology is applied over high priority data to enhance the security of the sensitive information. Then, the low priority data is stored into a separate log file for later use. Comparison results have shown that the proposed system outperforms existing systems.
Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.